README – Replication materials
“Narrating Security, Preserving Identity: Japan’s Discursive Securitization Without Militarization”

This Dataverse deposit contains the materials necessary to replicate the empirical analyses reported in the article. The study is a qualitative, computational text-as-data analysis that combines Semantic Network Analysis (SNA) and topic modeling (LDA) to explore the hidden themes of Japan’s Diet deliberations on the Russia–Ukraine conflict and, through them, the principal dimensions of national identity.

All replication steps and analytical procedures are documented in:
- The Data and Methods section of the published article; and
- FPA_Replication-Instructions.md in this Dataverse deposit.

To reproduce the results, please:
1. Consult the article’s Data and Methods section for the conceptual and methodological overview; and
2. Follow the step-by-step instructions in Replication_Instructions_FPA.pdf together with the notebook FPA_Replication.ipynb.

This README only lists and briefly describes the files included in the replication package.


Data

(1) Source: National Diet Minutes Retrieval System (https://kokkai.ndl.go.jp/)
(2) Corpus: 1,974 meeting transcripts (Jan 1, 2022 – Jun 30, 2023)
(3) Article focus: Diet deliberations on the Russia–Ukraine conflict (keyword: “Ukraine”)
(4) Preprocessed corpus used in the code: output_with_date_u.csv (text + date)


Key replication files

（1）output_with_date_u.csv – input for all Python analyses
（2）stopwords.csv – Japanese stopword list
（3）FPA_Replication-Program.ipynb – full replication notebook (Google Colab–ready)
（4）lda_vis_4topics_modified.html – pyLDAvis topic-model visualization (representative run)
（5）FPA_Replication-Instructions.md – short technical note on the LDA script and stochasticity


Note on LDA stochasticity

LDA is a stochastic model; repeated runs on the same data may yield slightly different numerical values. Under the fixed preprocessing and model settings in FPA_Replication.ipynb, the semantic content of topics and their temporal patterns remain stable. The HTML file lda_vis_4topics_modified.html and the included screenshot correspond to a representative run and to the original Google Colab output underlying Figure 5.